{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/component-aware-anomaly-detection-framework","title":"Component-aware anomaly detection framework for adjustable and logical industrial visual inspection","arxiv_id":"2305.08509","date":"2023-05-15","proceeding":null,"authors":["Tongkun Liu","Bing Li","Xiao Du","Bingke Jiang","Xiao Jin","Liuyi Jin","Zhuo Zhao"],"abstract":"Industrial visual inspection aims at detecting surface defects in products during the manufacturing process. Although existing anomaly detection models have shown great performance on many public benchmarks, their limited adjustability and ability to detect logical anomalies hinder their broader use in real-world settings. To this end, in this paper, we propose a novel component-aware anomaly detection framework (ComAD) which can simultaneously achieve adjustable and logical anomaly detection for industrial scenarios. Specifically, we propose to segment images into multiple components based on a lightweight and nearly training-free unsupervised semantic segmentation model. Then, we design an interpretable logical anomaly detection model through modeling the metrological features of each component and their relationships. Despite its simplicity, our framework achieves state-of-the-art performance on image-level logical anomaly detection. Meanwhile, segmenting a product image into multiple components provides a novel perspective for industrial visual inspection, demonstrating great potential in model customization, noise resistance, and anomaly classification. The code will be available at https://github.com/liutongkun/ComAD.","url_abs":"https://arxiv.org/abs/2305.08509v1","url_pdf":"https://arxiv.org/pdf/2305.08509v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"component-aware-anomaly-detection-framework","repo_url":"https://github.com/liutongkun/comad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-classification","task_name":"Anomaly Classification"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-semantic-segmentation","task_name":"Unsupervised Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-loco-ad","task":"Anomaly Detection","dataset":"MVTec LOCO AD","model":"ComAD+PatchCore","rank_in_archive_order":9,"of":40,"metrics":{"Avg. Detection AUROC":"90.1","Detection AUROC (only logical)":"89.4","Detection AUROC (only structural)":"90.9"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-loco-ad","task":"Anomaly Detection","dataset":"MVTec LOCO AD","model":"ComAD+AST","rank_in_archive_order":11,"of":40,"metrics":{"Avg. Detection AUROC":"89.8","Detection AUROC (only logical)":"90.1","Detection AUROC (only structural)":"89.4"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-loco-ad","task":"Anomaly Detection","dataset":"MVTec LOCO AD","model":"ComAD+RD4AD","rank_in_archive_order":13,"of":40,"metrics":{"Avg. Detection AUROC":"88.2","Detection AUROC (only logical)":"87.5","Detection AUROC (only structural)":"88.8"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-loco-ad","task":"Anomaly Detection","dataset":"MVTec LOCO AD","model":"ComAD+DRAEM","rank_in_archive_order":15,"of":40,"metrics":{"Avg. Detection AUROC":"87.9","Detection AUROC (only logical)":"85.9","Detection AUROC (only structural)":"89.9"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-loco-ad","task":"Anomaly Detection","dataset":"MVTec LOCO AD","model":"ComAD","rank_in_archive_order":25,"of":40,"metrics":{"Avg. Detection AUROC":"81.2","Detection AUROC (only logical)":"87.7","Detection AUROC (only structural)":"74.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.08509","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}